AlignUI: A Method for Designing LLM-Generated UIs Aligned with User Preferences

2026-01-24

AI Summary

This work proposes a task-aware user interface (UI) generation approach based on large language models (LLMs) to address the inefficiency of traditional UI design in adapting to individual user preferences, which typically requires extensive iteration and testing. Leveraging a crowdsourced, multidimensional dataset of user preferences, the method employs prompt engineering to guide LLMs in reasoning about and generating UIs aligned with user needs. Evaluated across six previously unseen tasks by 72 users, the generated interfaces significantly outperform baseline approaches across multiple preference dimensions, demonstrating the capability to produce personalized UIs with high cross-task alignment and fidelity to user requirements.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Machine Learning: Learning Preferences or Rankings
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Designing user interfaces that align with user preferences is a time-consuming process, which requires iterative cycles of prototyping, user testing, and refinement. Recent advancements in LLM-based UI generation have enabled efficient UI generation to assist the UI design process. We introduce AlignUI, a method that aligns LLM-generated UIs with user tasks and preferences by using a user preference dataset to guide the LLM's reasoning process. The dataset was crowdsourced from 50 general users (the target users of generated UIs) and contained 720 UI control preferences on eight image-editing tasks. We evaluated AlignUI by generating UIs for six unseen tasks and conducting a user study with 72 additional general users. The results showed that the generated UIs closely align with multiple dimensions of user preferences. We conclude by discussing the applicability of our method to support user-aligned UI design for multiple task domains and user groups, as well as personalized user needs.

Problem

• user interface design
• user preferences
• LLM-generated UIs
• preference alignment
• human-centered AI

Innovation

• LLM-based UI generation
• user preference alignment
• crowdsourced preference dataset
• user-centered design
• personalized UI